The Future of Artificial Intelligence in Banking

Table of Contents

1. Executive Summary 3
1.1. Market summary 3
1.2. Key findings 3
1.3. Critical success factors 3

2. Ai Will Transform Retail Banking 7
2.1. What is AI? 7
2.2. What impact will AI have on banks? 7
2.3. Which AI applications are relevant for banking? 8
2.3.1. RPA 8
2.3.2. NLP 9
2.3.3. Advanced data analytics 10
2.3.4. Image analytics 11
2.3.5. ML and deep learning 12

3. Ai Will Impact Banking In Several Ways 13
3.1. Customer-facing implementations 13
3.1.1. Chatbots and virtual assistants 13
3.1.2. PFM 17
3.1.3. Identity verification using biometric data or document scanning 20
3.2. Back-office implementations 24
3.2.1. Anti-money laundering and fraud detection 24
3.2.2. Underwriting and credit assessment 26

4. Recommendations For Implementing Ai 29
4.1. Improve data quality 29
4.2. Partner with fintech specialists 29
4.3. Plan for potential execution risks 30
4.3.1. Malfunctions and lack of efficacy 30
4.3.2. Algorithm bias 30
4.3.3. Lack of transparency 30
4.3.4. Data privacy issues 31

5. Appendix 32
5.1. Abbreviations and acronyms 32
5.2. Bibliography 32
5.3. Further reading 34

List of Figures

Figure 1: AI will enable increasingly sophisticated analysis of data for the benefit of customers 11
Figure 2: Erica provides proactive financial insight and advice to Bank of America customers 14
Figure 3: Personetics Anywhere uses a conversational interface to convey information to customers 15
Figure 4: Plum, Ernest, and Olivia aim to encourage better financial behavior through intelligent alerts and prompts 19
Figure 5: Wells Fargo is one of several dozen banks that offers Eyeprint login 21
Figure 6: USAA was one of the first US providers to offer facial recognition 22
Figure 7: Video-Ident from IDnow reads the holographic information in German ID cards to confirm authenticity 23
Figure 8: TrustingSocial uses social media data to verify consumers' identities and check their creditworthiness 27